You have three options for running the tutorial:
- Writing matlab code and running on your laptop: download or fork matlab directory (requires Matlab and some Toolboxes)
- Writing python code and running on your laptop: download or fork python directory. (requires Python and some data analysis packages: see crcns-student/python/getting_started.ipynb)
- Running python code on the cloud using Colab. When using Colab, you will need to make a copy of the notebook into your own Google Drive if you want to save your progress. Currently the Colab is set to UC Berkeley access only. Follow the links and request access if you don't have UC Berkeley credentials.
Options 1 and 2 - require that you download the data directory as well.
To download the data you can type:
python download_data.py
or get it by url directly from dropbox: 'https://www.dropbox.com/s/m9riuepg5xfdyoi/data.zip?dl=0'
Colab links:
- Tutorial # 1 Direct Fit https://colab.research.google.com/drive/1HWx3kWMAM2kLZ8Sf9KSr_p07LqanOvwX?usp=sharing
- Tutorial # 2 Gradient Descent https://colab.research.google.com/drive/1tQaSvGzkNvGYfPq9TxCt28ehif7gfkd0?usp=sharing
- Tutorial # 3 Logistic Regression
- Tutorial # 4 Coherence